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Thompson Sampling

Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists of choosing the action that maximizes the expected reward with respect to a randomly drawn belief.

Papers

Showing 5160 of 655 papers

TitleStatusHype
RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health InterventionsCode0
Bayesian Optimization for Categorical and Category-Specific Continuous InputsCode0
Efficient Exploration through Bayesian Deep Q-NetworksCode0
Efficient Optimal Selection for Composited Advertising Creatives with Tree StructureCode0
Bayesian Non-stationary Linear Bandits for Large-Scale Recommender SystemsCode0
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte CarloCode0
Scalable Exploration via Ensemble++Code0
FedRTS: Federated Robust Pruning via Combinatorial Thompson SamplingCode0
Improving Portfolio Optimization Results with Bandit NetworksCode0
Addressing Missing Data Issue for Diffusion-based RecommendationCode0
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